Salīdzināt metodes

	Telpiskās pretfaktuālu ietekmes novērtēšana (SCIE)	Instrumentālo mainīgo (IV) metode cēloņsakarību noteikšanai
Nozare	Cēloņsakarību secināšana	Veselības ekonomika
Saime	Regression model	Process / pipeline
Izcelsmes gads	2010s	1990s (modern applications)
Autors	Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tips	Quasi-experimental / causal inference	Method
Pirmavots	Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Citi nosaukumi	SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis	IV, two-stage least squares, TSLS, causal estimation
Saistītās	5	3
Kopsavilkums	Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.